Next Article in Journal
Optical Parameters Optimization for All-Time Star Sensor
Previous Article in Journal
Highly Selective, ppb-Level Xylene Gas Detection by Sn2+-Doped NiO Flower-Like Microspheres Prepared by a One-Step Hydrothermal Method
Previous Article in Special Issue
A Smart Phone Based Handheld Wireless Spirometer with Functions and Precision Comparable to Laboratory Spirometers
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

1
School of Engineering, Taylor’s University, 1, Jalan Taylor’s, Subang Jaya, Selangor 47500, Malaysia
2
Faculty of Sciences and Technologies, University of Sidi Mohamed Ben Abdellah, Route Imouzzer Fez, BP 2626, Fes 30000, Morocco
3
Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
4
Simulation Metier, Valeo India Pvt Ltd., 1/396, Old Mahabalipuram Road, Navallur, Chennai, Tamil Nadu 600130, India
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 2959; https://doi.org/10.3390/s19132959
Received: 7 May 2019 / Revised: 9 June 2019 / Accepted: 25 June 2019 / Published: 4 July 2019
(This article belongs to the Special Issue Wireless Sensing Systems for Body Area Networks)
  |  
PDF [5159 KB, uploaded 4 July 2019]
  |     |  

Abstract

Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment. View Full-Text
Keywords: gesture recognition; CSI; Wi-Fi; HOS; cumulants; mutual information; SVM gesture recognition; CSI; Wi-Fi; HOS; cumulants; mutual information; SVM
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Farhana Thariq Ahmed, H.; Ahmad, H.; Phang, S.K.; Vaithilingam, C.A.; Harkat, H.; Narasingamurthi, K. Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices. Sensors 2019, 19, 2959.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top